[53] Chen Tessler, Yonathan Efroni, and Shie Mannor. Action robust reinforcement learning and applications in continuous control. In International Conference on Machine Learning, pages 6215-6224. PMLR, 2019.
[54] Masatoshi Uehara and Wen Sun. Pessimistic model-based offline reinforcement learning under partial coverage. arXiv preprint arXiv:2107.06226, 2021.
[55] Sara A Van de Geer. Empirical Processes in M-estimation, volume 6. Cambridge university press, 2000.
[56] Lu Wang, Wei Zhang, Xiaofeng He, and Hongyuan Zha. Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2447-2456, 2018.
[57] Qiuhao Wang, Chin Pang Ho, and Marek Petrik. On the convergence of policy gradient in robust mdps. arXiv preprint arXiv:2212.10439, 2022.
[58] Shengbo Wang, Nian Si, Jose Blanchet, and Zhengyuan Zhou. A finite sample complexity bound for distributionally robust q-learning. In International Conference on Artificial Intelligence and Statistics, pages 3370–3398. PMLR, 2023.
[59] Yue Wang and Shaofeng Zou. Online robust reinforcement learning with model uncertainty. Advances in Neural Information Processing Systems, 34:7193-7206, 2021.
[60] Yue Wang and Shaofeng Zou. Policy gradient method for robust reinforcement learning. In International Conference on Machine Learning, pages 23484-23526. PMLR, 2022.
[61] Wolfram Wiesemann, Daniel Kuhn, and Berç Rustem. Robust markov decision processes. Mathematics of Operations Research, 38(1):153-183, 2013.
[62] Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, and Alekh Agarwal. Bellman-consistent pessimism for offline reinforcement learning. Advances in neural information processing systems, 34:6683-6694, 2021.
[63] Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, and Yu Bai. Policy finetuning: Bridging sample-efficient offline and online reinforcement learning. Advances in neural information processing systems, 34:27395-27407, 2021.
[64] Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, and Tong Zhang. Nearly minimax optimal offline reinforcement learning with linear function approximation: Single-agent mdp and markov game. arXiv preprint arXiv:2205.15512, 2022.
[65] Huan Xu and Shie Mannor. Distributionally robust markov decision processes. Advances in Neural Information Processing Systems, 23, 2010.
[66] Zaiyan Xu, Kishan Panaganti, and Dileep Kalathil. Improved sample complexity bounds for distributionally robust reinforcement learning. In International Conference on Artificial Intelligence and Statistics, pages 9728-9754. PMLR, 2023.
[67] Greg Yang and Hadi Salman. A fine-grained spectral perspective on neural networks. arXiv preprint arXiv:1907.10599, 2019.
[68] Wenhao Yang, Liangyu Zhang, and Zhihua Zhang. Towards theoretical understandings of robust markov decision processes: Sample complexity and asymptotics. arXiv preprint arXiv:2105.03863, 2021.
[69] Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M Jordan, and Zhihua Zhang. Avoiding model estimation in robust markov decision processes with a generative model. arXiv preprint arXiv:2302.01248, 2023.
[70] Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, and Michael Jordan. Provably efficient reinforcement learning with kernel and neural function approximations. Advances in Neural Information Processing Systems, 33:13903-13916, 2020.